Speaker
Mr
Cristiano Fanelli
(INFN Sezione di Roma, Universit\`a di Roma `La Sapienza', Roma, Italy)
Description
A new tracker based on the GEM technology is under development for the upcoming experiments in Hall A at Jefferson Lab, where a longitudinally polarized electron beam of 11 GeV, combined with innovative polarized targets, will provide luminosity up to 10$^{39}$/(s cm$^{2}$) opening exciting opportunities to investigate unexplored aspects of the inner structure of the nucleon and the dynamics of its constituents.
At this luminosity, the expected background flux, mainly due to low energy ($\sim 1$ MeV) photons,
is up to 500 MHz/cm$^2$ which generates about 200 kHz/cm$^2$ hits in each tracker chamber.
In such a context, an efficient, computational time effective and precise tracks reconstruction is mandatory.
We propose a novel algorithm based on a Hopfield neural network (NN) combined to filter techniques.
A preliminary clustering of the GEM hits exploits all spatial and timing information of the acquired signals coming from the GEM strips, to maximally reduce the data to be processed.
The NN, within a mean field theory framework, provides a robust association of the GEM hits,
(then drastically reducing the potential hit combinations),
whereas a Kalman filter associated to a Rauch-Tung-Striebel smoother, is used for final accurate reconstruction.
Results of the first tests on simulated and real data will be presented as well
as a description of the method and of its original aspects.
Authors
Mr
Alessio Del Dotto
(Universit\`a di Roma Tre and INFN, Roma, Italy)
Mr
Cristiano Fanelli
(INFN Sezione di Roma, Universit\`a di Roma `La Sapienza', Roma, Italy)
Dr
Evaristo Cisbani
(INFN, Sezione di Roma, gruppo Sanit\`a and Istituto Superiore di Sanit\`a, I-00161 Rome, Italy)
